We need to talk. Measuring intercultural dialogue for peace and inclusion
Bibliographic record
Abstract
An analysis into the power of intercultural dialogue and the new UNESCO Framework for Enabling Intercultural Dialogue, We Need to Talk presents the first evidence of the link between intercultural dialogue and peace, conflict prevention and non-fragility, and human rights. Using data covering over 160 countries in all regions, the report presents a framework of the structures, processes and values needed to support intercultural dialogue, examining the dynamics and interlinkages between them to reveal substantial policy opportunities with broad spanning benefits. Providing policy support and guidance, the report also includes information on regional trends as well as deep diving case studies. The data, case studies, and think pieces contained in this report highlight key policy and intervention opportunities for intercultural dialogue as an instrument for inclusion, peace and wider societal benefits. Policy makers, development workers, peace and security actors, academics and more are invited to leverage the analysis in this report and findings of the Framework to strengthen intercultural dialogue around the world. UNESDOC Catno 0000382874
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.031 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".